




import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 					__lowerCamelCase			(       UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = BlenderbotSmallTokenizer
    snake_case__   = False


    def   a (			self	:	List[str]  ) ->			List[str]:
         super().setUp()

         lowerCAmelCase__            =					["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
         lowerCAmelCase__            =					dict(zip(SCREAMING_SNAKE_CASE__  ,      range(len(SCREAMING_SNAKE_CASE__  )  )  )  )

         lowerCAmelCase__            =					["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
         lowerCAmelCase__            =					{"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

         lowerCAmelCase__            =					os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["vocab_file"]  )
         lowerCAmelCase__            =					os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["merges_file"]  )
         with open(self.vocab_file  ,      "w"  ,      encoding="utf-8"  ) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE__  ) + "\n"  )
         with open(self.merges_file  ,      "w"  ,      encoding="utf-8"  ) as fp:
              fp.write("\n".join(SCREAMING_SNAKE_CASE__  )  )


    def   a (			self	:	Any  ,      **SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			List[Any]:
         kwargs.update(self.special_tokens_map  )
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Optional[Any]:
         lowerCAmelCase__            =					"adapt act apte"
         lowerCAmelCase__            =					"adapt act apte"
         return input_text, output_text


    def   a (			self	:	Dict  ) ->			Tuple:
         lowerCAmelCase__            =					BlenderbotSmallTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map  )
         lowerCAmelCase__            =					"adapt act apte"
         lowerCAmelCase__            =					["adapt", "act", "ap@@", "te"]
         lowerCAmelCase__            =					tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
         self.assertListEqual(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         lowerCAmelCase__            =					[0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ) ->			Tuple:
         lowerCAmelCase__            =					BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )
         assert tok("sam"  ).input_ids == [1_384]
         lowerCAmelCase__            =					"I am a small frog."
         lowerCAmelCase__            =					tok([src_text]  ,      padding=SCREAMING_SNAKE_CASE__  ,      truncation=SCREAMING_SNAKE_CASE__  )["input_ids"]
         lowerCAmelCase__            =					tok.batch_decode(SCREAMING_SNAKE_CASE__  ,      skip_special_tokens=SCREAMING_SNAKE_CASE__  ,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__  )[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."


    def   a (			self	:	Optional[Any]  ) ->			int:
         lowerCAmelCase__            =					BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )
         lowerCAmelCase__            =					"I am a small frog ."
         lowerCAmelCase__            =					"."
         lowerCAmelCase__            =					tok(SCREAMING_SNAKE_CASE__  )["input_ids"]
         lowerCAmelCase__            =					tok(SCREAMING_SNAKE_CASE__  )["input_ids"]

         assert encoded[-1] == encoded_dot[0]




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




